Dear List,


I am trying to fit a logistic mixed model with two levels of nesting, e.g.:

MCMCglmm( y ~ fixef1 + ... + fixefN, random=~ ranef1 + ranef2, prior=GoodPrior, ...)

I would like to apply parameter expansion to speed up things (as I would need to fit this repeatedly).

GoodPrior <- list(G=list(G1=list(V=1, nu=1, alpha.mu=0, alpha.V=1000),
                            G2=list(V=1, nu=1, alpha.mu=0, alpha.V=1000)),
                     R=list(V=1, nu=0.002))


Question 1:
When I use this parameter expanded approach in the way described above, I believe there is no need for any rescaling afterwards.
Correct?

Question 2:
How do I determine appropriate alpha.mu and alpha.V? These values I took from the course notes by Jarrod Hadfield

Question 3:
In the course notes there is an example provided with rescaling location effects (what is meant by this?) and variances?
It is unclear to me how you arrive at this variable with which you scale back:
c2 <- (16*sqrt(3)/(15*pi)^2

where does this come from exactly?


Any help is appreciated. A pointer to an accessible reference as well; I could not find one answering these questions.

Kind regards,
Louis








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